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unit1 Introduction to health Economics

Health Economics
Health Economics
• …studies the supply and demand of health care resources and
the impact of health care resources on a population. (Mosby
Medical Encyclopedia 1992, p. 361)
The 4 Basic Questions
What combination of nonmedical and medical goods
and services should be produced in the macro
What particular medical goods and services should be
produced in the health economy?
What specific health care resources should be used to
produce the final medical goods and services?
Who should receive the medical goods and services?
The 4 Basic Questions
1) What combination of nonmedical and medical
goods and services should be produced in the
macro economy?
a) Should the government spend more on missile
defense or to reduce the number of uninsured
b) Should the government spend more on education or
prescription drugs for the elderly?
The 4 Basic Questions (cont.)
2) What particular medical goods and
services should be produced in the health
a) Should the government spend more on
prescription drugs for the elderly or on home
health care?
b) Should the government spend more on
research to cure cancer or on cancer
screening programs?
The 4 Basic Questions (cont.)
3) What specific health care resources should be
used to produce the final medical goods and
a) Should more ambulatory patients be cared for by
nurse practitioners instead of physicians?
b) When is medical vs. surgical management of a health
condition appropriate?
The 4 Basic Questions (cont.)
4) Who should receive the medical goods
and services?
a) Should all of the elderly receive prescription
drug coverage from the gov’t, or only the
low-income elderly?
b) Should refugees receive free care in
community clinics and hospitals?
• Because resources are limited, health economists
are concerned with determining what medical
services to produce, how they should be
produced, and who should receive them
• As we will see in this course, the tools of
economics can be applied to the health care
sector to derive valuable insights about our health
care system
Health System Goals
• Access to care
– Who’s covered?
– What’s covered?
• Quality of care
– Medical efficacy
– Medical outcomes
• Cost of care
– Who pays?
– How much?
Normative versus Positive Analysis of the
Health Sector
• Normative analysis deals with the appropriateness
or desirability of an economic outcome or policy.
– What ought to be?
– Which is better?
• Positive analysis makes statements or predictions
regarding economic behavior.
– What is?
– What happened?
Normative Economics VS Positive
Economics of the Health Sector
• Normative Economics ranks resource
allocations and the policies that generate
• Ranking a policy requires positive analysis
Normative Analysis in the Health Sector
2 Literatures:
• Normative analysis of the operation of health
care, health insurance market, market failure,
non-market institutional arrangements to
improve efficiency and equity of financing,
funding, organization and delivery of health care.
• What is the most appropriate normative
framework within which to carry out analysis in
the health sector, focusing on welfarist and extrawelfarist frameworks?
Efficiency and Normative Frameworks
• Efficiency: the extent to which time or effort is well
used for the intended task or purpose. (Doing things
in the most economical way)
– Technical Efficiency: Production is organized to minimize
the inputs required to produce a given output
– Production efficiency: Production is organized to minimize
the cost of producing a given output
– Allocative efficiency: (demand-side) resources are
produced and allocated so as to produce the optimal level
of each output
Neo-Classical Welfare Economic Framework
• Utility Maximization
– Behavioral assumption
– Individuals choose rationally
• Individual Sovereignty
– Individuals are the best judges of own welfare
• Consequentialism
– Any action, choice or policy judged in terms of consequent
effects. Outcome matters, not process
• Welfarism
– Goodness is judged on utility levels attained
Neo-Classical Welfare Economic Framework
• Two theorems of Welfare Economics
– The allocation of resources generated by a perfectly
competitive market process is Pareto Optimal
• Acheives all three levels of efficiency
– Any Pareto Optimal allocation can be achieved
through a perfectly competitive economy.
Welfare Economic Framework
• Individual Sovereignty
• Welfarism
– Willingness-to pay as a monetary metric for utility
• Welfare economics takes market allocation as
the reference standard
• Separation of efficiency and equity
– Exclusive focus on efficiency
Critiques of Welfare Economics in the
Health Sector
• Assumption of Individual Sovereignty is
violated in health sector
• Willingness to pay loses it normative
– Value of a health care service to an individual is
not accurately represented by WTP
• When health care service is a life or death issue, its
value or benefit not linked to ability to pay of an
• Utility is not the only relevant argument
– Health, not utility is most important outcome for normative
– Health care should be allocated to maximize level of health of
the nation instead of the utility consumers derive as they
utilize the health services (Feldstein, 1963)
• The concept of need (as opposed to demand)
Positive Economics of the Health Sector
• Health Econometrics
– Econometrics literature in health economics
– Translating Heath economics to a wider audience
• Identification and Estimation
– The evaluation problem
– Are we able to identify causal effects from
empirical data?
The Evaluation Problem
– Randomized experimental design
• Individuals are randomly assigned to the treatment
group and control group (RCTs)
– Natural experiment
• Look like a controlled experiment
• Mechanism that assigns individuals to treatment and
control groups is not random
• Event happens naturally that affects some and not
• Naturally occurring event is assigned as if random
The Evaluation Problem
• Most econometric studies rely on
observational data, gathered in a nonexperimental setting
• Data are prone to problems of non-random
selection and measurement error, which may
bias estimates of causal effects
Model Specification
• Finding an appropriate stochastic model to fit
available data
• Many times classical regression analysis assumptions
are not met with available healthcare data
– Linearity, normal distribution of error term
• Individual level survey data common in healthcare
are nonlinear
Examples of Nonlinear data
• Binary responses in survey data (0,1)
• Multinomial responses (0,1,2,3)
• Limited dependent variables:
– expenditure data (censored at 0)
• Integer counts
– Number of visits to the Dr.
• Duration measures
– Time elapsed between visits
Nonlinear Models in Health Economics
• Maximum likelihood (ML) estimation
– Properties: consistent, asymptotic normality
– ML models need to be fully and correctly specified
– Used for qualitative or limited dependent variables
• Pseudo or quasi maximum likelihood (PML)
– Models do not have to be correctly specified
– PML estimator of the conditional mean is consistent and
asymptotically normal for binomial, normal, gamma and
Poisson distributions
– PML used in count data regressions in health economics
Qualitative Dependent Variables
• Binary Responses
– Binary dependent variable
• Participant or non-participant in health insurance; smoker vs
non smoker, etc
E(yi|xi) = P(yi =1|xi)= F(xi)
• Could be estimated as a linear function
– weighted least squares
• Could be estimated as nonlinear function
– Logit model (assumes standard logistic distribution)
– Probit model (assumes standard normal distribution)
Qualitative Dependent Variables
• Multinomial and ordered responses
– Ordered probits, grouped data regressions
• e.g. Self assessed health: excellent, good, fair, poor
• Average daily ciggarete consumption
– Multinomial logit
– Nested multinomial logit
• Impact of user fees on the demand for medical care in
• Decision to seek care; condition on decision on which
provider to use (public, hospital, private doc)
– Multinomial probit model
Limited Dependent Variables
• Two part models,
• Selectivity models
– Semiparametric estimators,
• Hurdle models
– Box-Cox double hurdle model
Estimation Strategies in Health
• Estimating treatment effects
– Longitudinal (panel) data: repeated
measurements for a person allows opportunity to
control for unobservable individual effects which
remain constant over time.
– Instrumental Variables: Instruments are good
predictors of the treatment, and not related to the
– Control function approaches to selection bias
• Parametric and non-parametric methods
Bootstrapping Methodology
• Many estimators in HE rely on asymptotic theory
• When asymptotic theory is unknown or finite sample
properties of an estimator are unknown, use bootstrapping .
• Bootstrapping aims to reduce bias and provide more reliable
confidence intervals
• Boostrap data: repeated re-sampling of estimation data using
random sampling with replacement
• The bootstrap sample is then used to approximate the
sampling distribution of estimator being used.
Unobservable heterogeneity and
simultaneous models
• Linear models
– Instrumental variables
– MIMIC models
• Nonlinear models
– Switching regressions
– Simultaneous estimators
Longitudinal and Hierarchical data
• Multilevel models
• Panel Data
– Random versus fixed effects
Nonparametric and semiparametric
• Estimators rely on assumptions about
– functional form of the regression equation
– Distribution of the error term
• Rosenblatt-Parzen kernel density estimator
– Uses weighted local averages to estimate
probability density functions of unknown forms
Count data regression
• Count data
– Dependent variable is a non-negative integervalued count,
– Y=0,1,2,….
– Used when dependent variable is skewed to left
– Contains large numbers of zeros
– E.g. health care utilizations, # of prescriptions
– # of drinks over 2 weeks
Duration analysis
• Survival and Duration data
– ‘Time until failure’
– E.g. individual life span; mortality rates
– Used in context of individual health production